Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
!ls -al /input
total 8308
drwxr-xr-x   4 root root    6144 Apr 29 00:27 .
drwxr-xr-x 138 root root    4096 Aug 16 15:51 ..
drwxr-xr-x   2 root root 6137856 Apr 28 19:01 img_align_celeba
drwxr-xr-x   2 root root 2365440 Apr 28 18:57 mnist
In [2]:
data_dir = '/input'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[3]:
<matplotlib.image.AxesImage at 0x7f8ff02ac668>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[4]:
<matplotlib.image.AxesImage at 0x7f8ff0229748>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [5]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [6]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    
    real_inputs = tf.placeholder(
        tf.float32, 
        (None, image_height, image_width, image_channels),
        name='real_inputs'
    )
    z_inputs = tf.placeholder(tf.float32, (None, z_dim), name='z_inputs')
    lrate = tf.placeholder(tf.float32, name='lrate')
    return real_inputs, z_inputs, lrate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [33]:
def discriminator(images, reuse=False, alpha=0.2, kernel=5, filters=32):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # Input layer is 28x28x3
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(images, filters, kernel, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x32
        
        x2 = tf.layers.conv2d(x1, filters*2, kernel, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x64
        
        x3 = tf.layers.conv2d(x2, filters*2, kernel, strides=1, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        flat = tf.reshape(relu3, (-1, 7*7*filters*2))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [32]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2, kernel=5):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse=not is_train):
        x1 = tf.layers.dense(z, 7*7*512)
        
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x512
        
        x2 = tf.layers.conv2d_transpose(x1, 256, kernel, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 14x14x256
        
        x3 = tf.layers.conv2d_transpose(x2, 128, kernel, strides=1, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 14x14x128
        
        logits = tf.layers.conv2d_transpose(
            x3, out_channel_dim, kernel, strides=2, padding='same')
        # 28x28x3
        
        out = tf.tanh(logits)
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [34]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    gen_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(gen_model, reuse=True)
    
    ones_like_real = tf.ones_like(d_model_real)
    one_sided_smooth_labels = tf.multiply(
        ones_like_real,
        tf.random_uniform((1,), minval=0.8, maxval=1.2)
    )

    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_real, labels=one_sided_smooth_labels
        )
    )
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)
        )
    )
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_fake, labels=tf.ones_like(d_model_fake)
        )
    )
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [27]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [35]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [29]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    steps=0
    
    # TODO: Build Model
    image_channels = 3 if data_image_mode == 'RGB' else 1
    image_height, image_width = data_shape[1], data_shape[2]
    real_inputs, z_inputs, lrate = model_inputs(
        image_width, image_height, image_channels, z_dim)
        
    d_loss, g_loss = model_loss(real_inputs, z_inputs, image_channels)
    
    d_opt, g_opt = model_opt(d_loss, g_loss, lrate, beta1)
        
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                
                steps += 1
                batch_images = 2 * batch_images
                
                batch_z = np.random.uniform(-1 ,1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={
                    real_inputs: batch_images,
                    z_inputs: batch_z,
                    lrate: learning_rate
                })
                
                # Double the number of trains to generator
                _ = sess.run(g_opt, feed_dict={
                    z_inputs: batch_z,
                    real_inputs: batch_images,
                    lrate: learning_rate
                })
                
                
                if steps % 10 == 0:
                    # At the end of every 10 epochs, get the losses and print them out
                    train_loss_d = d_loss.eval({z_inputs: batch_z, real_inputs: batch_images})
                    train_loss_g = g_loss.eval({z_inputs: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g),
                          "Sum Loss: {:.4f}".format(train_loss_g+train_loss_d))
                
                if steps % 100 == 0:
                    show_generator_output(
                        sess,
                        25,
                        z_inputs,
                        image_channels,
                        data_image_mode
                    )
                  
        show_generator_output(sess, 25, z_inputs, image_channels, data_image_mode)

                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
 
In [36]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 3.6253... Generator Loss: 0.0503 Sum Loss: 3.6756
Epoch 1/2... Discriminator Loss: 2.3841... Generator Loss: 0.1784 Sum Loss: 2.5625
Epoch 1/2... Discriminator Loss: 1.4971... Generator Loss: 0.4791 Sum Loss: 1.9762
Epoch 1/2... Discriminator Loss: 1.1332... Generator Loss: 0.8109 Sum Loss: 1.9441
Epoch 1/2... Discriminator Loss: 1.5399... Generator Loss: 0.4034 Sum Loss: 1.9434
Epoch 1/2... Discriminator Loss: 1.9564... Generator Loss: 0.4255 Sum Loss: 2.3820
Epoch 1/2... Discriminator Loss: 2.0439... Generator Loss: 0.4896 Sum Loss: 2.5334
Epoch 1/2... Discriminator Loss: 1.8633... Generator Loss: 0.5892 Sum Loss: 2.4524
Epoch 1/2... Discriminator Loss: 1.9537... Generator Loss: 0.5654 Sum Loss: 2.5191
Epoch 1/2... Discriminator Loss: 1.7833... Generator Loss: 0.7366 Sum Loss: 2.5199
Epoch 1/2... Discriminator Loss: 1.7388... Generator Loss: 0.4608 Sum Loss: 2.1996
Epoch 1/2... Discriminator Loss: 1.8868... Generator Loss: 0.5099 Sum Loss: 2.3966
Epoch 1/2... Discriminator Loss: 1.6296... Generator Loss: 0.5556 Sum Loss: 2.1852
Epoch 1/2... Discriminator Loss: 1.7814... Generator Loss: 0.4887 Sum Loss: 2.2700
Epoch 1/2... Discriminator Loss: 1.6667... Generator Loss: 0.5614 Sum Loss: 2.2282
Epoch 1/2... Discriminator Loss: 1.7624... Generator Loss: 0.4711 Sum Loss: 2.2335
Epoch 1/2... Discriminator Loss: 1.6784... Generator Loss: 0.4622 Sum Loss: 2.1406
Epoch 1/2... Discriminator Loss: 1.8014... Generator Loss: 0.7300 Sum Loss: 2.5314
Epoch 1/2... Discriminator Loss: 1.5996... Generator Loss: 0.5345 Sum Loss: 2.1341
Epoch 1/2... Discriminator Loss: 1.6038... Generator Loss: 0.7303 Sum Loss: 2.3341
Epoch 1/2... Discriminator Loss: 1.5956... Generator Loss: 0.5383 Sum Loss: 2.1339
Epoch 1/2... Discriminator Loss: 1.5404... Generator Loss: 0.4384 Sum Loss: 1.9788
Epoch 1/2... Discriminator Loss: 1.5888... Generator Loss: 0.5648 Sum Loss: 2.1536
Epoch 1/2... Discriminator Loss: 1.7233... Generator Loss: 0.4697 Sum Loss: 2.1930
Epoch 1/2... Discriminator Loss: 1.6912... Generator Loss: 0.6964 Sum Loss: 2.3876
Epoch 1/2... Discriminator Loss: 1.5208... Generator Loss: 0.5725 Sum Loss: 2.0933
Epoch 1/2... Discriminator Loss: 1.6163... Generator Loss: 0.8192 Sum Loss: 2.4354
Epoch 1/2... Discriminator Loss: 1.5648... Generator Loss: 0.7275 Sum Loss: 2.2923
Epoch 1/2... Discriminator Loss: 1.4843... Generator Loss: 0.6524 Sum Loss: 2.1368
Epoch 1/2... Discriminator Loss: 1.5006... Generator Loss: 0.5586 Sum Loss: 2.0591
Epoch 1/2... Discriminator Loss: 1.4394... Generator Loss: 0.7786 Sum Loss: 2.2180
Epoch 1/2... Discriminator Loss: 1.5165... Generator Loss: 0.5582 Sum Loss: 2.0747
Epoch 1/2... Discriminator Loss: 1.5668... Generator Loss: 0.8164 Sum Loss: 2.3832
Epoch 1/2... Discriminator Loss: 1.5364... Generator Loss: 0.4854 Sum Loss: 2.0217
Epoch 1/2... Discriminator Loss: 1.4410... Generator Loss: 0.7037 Sum Loss: 2.1447
Epoch 1/2... Discriminator Loss: 1.4431... Generator Loss: 0.6902 Sum Loss: 2.1333
Epoch 1/2... Discriminator Loss: 1.3973... Generator Loss: 0.7563 Sum Loss: 2.1536
Epoch 1/2... Discriminator Loss: 1.4505... Generator Loss: 0.6611 Sum Loss: 2.1117
Epoch 1/2... Discriminator Loss: 1.4222... Generator Loss: 0.6214 Sum Loss: 2.0436
Epoch 1/2... Discriminator Loss: 1.4217... Generator Loss: 0.8823 Sum Loss: 2.3040
Epoch 1/2... Discriminator Loss: 1.4150... Generator Loss: 0.5668 Sum Loss: 1.9819
Epoch 1/2... Discriminator Loss: 1.4930... Generator Loss: 0.8480 Sum Loss: 2.3410
Epoch 1/2... Discriminator Loss: 1.4910... Generator Loss: 0.5366 Sum Loss: 2.0276
Epoch 1/2... Discriminator Loss: 1.4988... Generator Loss: 0.4788 Sum Loss: 1.9776
Epoch 1/2... Discriminator Loss: 1.4111... Generator Loss: 0.7271 Sum Loss: 2.1382
Epoch 1/2... Discriminator Loss: 1.3890... Generator Loss: 0.6748 Sum Loss: 2.0638
Epoch 1/2... Discriminator Loss: 1.4416... Generator Loss: 0.5841 Sum Loss: 2.0257
Epoch 1/2... Discriminator Loss: 1.4523... Generator Loss: 0.6411 Sum Loss: 2.0933
Epoch 1/2... Discriminator Loss: 1.3939... Generator Loss: 0.6627 Sum Loss: 2.0566
Epoch 1/2... Discriminator Loss: 1.4407... Generator Loss: 0.7567 Sum Loss: 2.1974
Epoch 1/2... Discriminator Loss: 1.4069... Generator Loss: 0.7088 Sum Loss: 2.1157
Epoch 1/2... Discriminator Loss: 1.3818... Generator Loss: 0.6539 Sum Loss: 2.0357
Epoch 1/2... Discriminator Loss: 1.4325... Generator Loss: 0.6252 Sum Loss: 2.0577
Epoch 1/2... Discriminator Loss: 1.3938... Generator Loss: 0.6700 Sum Loss: 2.0638
Epoch 1/2... Discriminator Loss: 1.4118... Generator Loss: 0.7737 Sum Loss: 2.1856
Epoch 1/2... Discriminator Loss: 1.4249... Generator Loss: 0.6059 Sum Loss: 2.0308
Epoch 1/2... Discriminator Loss: 1.3778... Generator Loss: 1.0432 Sum Loss: 2.4210
Epoch 1/2... Discriminator Loss: 1.5019... Generator Loss: 0.9166 Sum Loss: 2.4185
Epoch 1/2... Discriminator Loss: 1.3453... Generator Loss: 0.5450 Sum Loss: 1.8903
Epoch 1/2... Discriminator Loss: 1.5116... Generator Loss: 0.9193 Sum Loss: 2.4309
Epoch 1/2... Discriminator Loss: 1.3886... Generator Loss: 0.8547 Sum Loss: 2.2433
Epoch 1/2... Discriminator Loss: 1.3963... Generator Loss: 0.6965 Sum Loss: 2.0928
Epoch 1/2... Discriminator Loss: 1.4129... Generator Loss: 0.6683 Sum Loss: 2.0811
Epoch 1/2... Discriminator Loss: 1.4510... Generator Loss: 0.5822 Sum Loss: 2.0331
Epoch 1/2... Discriminator Loss: 1.4097... Generator Loss: 0.6727 Sum Loss: 2.0825
Epoch 1/2... Discriminator Loss: 1.4114... Generator Loss: 0.7721 Sum Loss: 2.1835
Epoch 1/2... Discriminator Loss: 1.4006... Generator Loss: 0.7505 Sum Loss: 2.1511
Epoch 1/2... Discriminator Loss: 1.4045... Generator Loss: 0.7286 Sum Loss: 2.1331
Epoch 1/2... Discriminator Loss: 1.4002... Generator Loss: 0.8742 Sum Loss: 2.2744
Epoch 1/2... Discriminator Loss: 1.4192... Generator Loss: 0.7347 Sum Loss: 2.1540
Epoch 1/2... Discriminator Loss: 1.4914... Generator Loss: 0.4347 Sum Loss: 1.9261
Epoch 1/2... Discriminator Loss: 1.4020... Generator Loss: 0.5951 Sum Loss: 1.9971
Epoch 1/2... Discriminator Loss: 1.4565... Generator Loss: 0.4104 Sum Loss: 1.8670
Epoch 1/2... Discriminator Loss: 1.4331... Generator Loss: 0.6181 Sum Loss: 2.0512
Epoch 1/2... Discriminator Loss: 1.3907... Generator Loss: 0.5567 Sum Loss: 1.9475
Epoch 1/2... Discriminator Loss: 1.3879... Generator Loss: 0.8284 Sum Loss: 2.2163
Epoch 1/2... Discriminator Loss: 1.4416... Generator Loss: 0.6979 Sum Loss: 2.1395
Epoch 1/2... Discriminator Loss: 1.3603... Generator Loss: 0.8856 Sum Loss: 2.2459
Epoch 1/2... Discriminator Loss: 1.3563... Generator Loss: 0.8433 Sum Loss: 2.1995
Epoch 1/2... Discriminator Loss: 1.3986... Generator Loss: 0.7427 Sum Loss: 2.1413
Epoch 1/2... Discriminator Loss: 1.3666... Generator Loss: 0.8044 Sum Loss: 2.1710
Epoch 1/2... Discriminator Loss: 1.4522... Generator Loss: 0.8351 Sum Loss: 2.2873
Epoch 1/2... Discriminator Loss: 1.3839... Generator Loss: 0.7513 Sum Loss: 2.1352
Epoch 1/2... Discriminator Loss: 1.4171... Generator Loss: 0.6194 Sum Loss: 2.0365
Epoch 1/2... Discriminator Loss: 1.4765... Generator Loss: 0.9567 Sum Loss: 2.4333
Epoch 1/2... Discriminator Loss: 1.4008... Generator Loss: 0.6058 Sum Loss: 2.0066
Epoch 1/2... Discriminator Loss: 1.3743... Generator Loss: 0.8170 Sum Loss: 2.1913
Epoch 1/2... Discriminator Loss: 1.5893... Generator Loss: 0.4536 Sum Loss: 2.0429
Epoch 1/2... Discriminator Loss: 1.3731... Generator Loss: 0.7441 Sum Loss: 2.1173
Epoch 1/2... Discriminator Loss: 1.4772... Generator Loss: 0.5632 Sum Loss: 2.0403
Epoch 1/2... Discriminator Loss: 1.4091... Generator Loss: 0.6458 Sum Loss: 2.0549
Epoch 1/2... Discriminator Loss: 1.4260... Generator Loss: 0.6644 Sum Loss: 2.0904
Epoch 1/2... Discriminator Loss: 1.3921... Generator Loss: 0.5297 Sum Loss: 1.9218
Epoch 2/2... Discriminator Loss: 1.4493... Generator Loss: 0.9210 Sum Loss: 2.3703
Epoch 2/2... Discriminator Loss: 1.3725... Generator Loss: 0.5996 Sum Loss: 1.9721
Epoch 2/2... Discriminator Loss: 1.3554... Generator Loss: 0.7489 Sum Loss: 2.1043
Epoch 2/2... Discriminator Loss: 1.4505... Generator Loss: 0.6165 Sum Loss: 2.0670
Epoch 2/2... Discriminator Loss: 1.3836... Generator Loss: 0.6068 Sum Loss: 1.9905
Epoch 2/2... Discriminator Loss: 1.3942... Generator Loss: 0.6830 Sum Loss: 2.0772
Epoch 2/2... Discriminator Loss: 1.4944... Generator Loss: 0.5081 Sum Loss: 2.0025
Epoch 2/2... Discriminator Loss: 1.3682... Generator Loss: 0.6296 Sum Loss: 1.9978
Epoch 2/2... Discriminator Loss: 1.3933... Generator Loss: 0.6757 Sum Loss: 2.0690
Epoch 2/2... Discriminator Loss: 1.3647... Generator Loss: 0.6362 Sum Loss: 2.0009
Epoch 2/2... Discriminator Loss: 1.4293... Generator Loss: 0.7941 Sum Loss: 2.2234
Epoch 2/2... Discriminator Loss: 1.4398... Generator Loss: 0.7670 Sum Loss: 2.2068
Epoch 2/2... Discriminator Loss: 1.3622... Generator Loss: 0.5708 Sum Loss: 1.9329
Epoch 2/2... Discriminator Loss: 1.4056... Generator Loss: 0.6576 Sum Loss: 2.0632
Epoch 2/2... Discriminator Loss: 1.3448... Generator Loss: 0.7715 Sum Loss: 2.1163
Epoch 2/2... Discriminator Loss: 1.4757... Generator Loss: 0.4679 Sum Loss: 1.9436
Epoch 2/2... Discriminator Loss: 1.3776... Generator Loss: 0.6274 Sum Loss: 2.0050
Epoch 2/2... Discriminator Loss: 1.3784... Generator Loss: 0.6978 Sum Loss: 2.0762
Epoch 2/2... Discriminator Loss: 1.4070... Generator Loss: 0.6438 Sum Loss: 2.0508
Epoch 2/2... Discriminator Loss: 1.4187... Generator Loss: 0.7982 Sum Loss: 2.2170
Epoch 2/2... Discriminator Loss: 1.4335... Generator Loss: 0.5738 Sum Loss: 2.0073
Epoch 2/2... Discriminator Loss: 1.4737... Generator Loss: 0.9149 Sum Loss: 2.3885
Epoch 2/2... Discriminator Loss: 1.4070... Generator Loss: 0.8057 Sum Loss: 2.2127
Epoch 2/2... Discriminator Loss: 1.3791... Generator Loss: 0.8640 Sum Loss: 2.2431
Epoch 2/2... Discriminator Loss: 1.3853... Generator Loss: 0.6154 Sum Loss: 2.0007
Epoch 2/2... Discriminator Loss: 1.4811... Generator Loss: 0.7563 Sum Loss: 2.2374
Epoch 2/2... Discriminator Loss: 1.5082... Generator Loss: 0.8709 Sum Loss: 2.3790
Epoch 2/2... Discriminator Loss: 1.3556... Generator Loss: 0.5593 Sum Loss: 1.9149
Epoch 2/2... Discriminator Loss: 1.3723... Generator Loss: 0.5434 Sum Loss: 1.9157
Epoch 2/2... Discriminator Loss: 1.3732... Generator Loss: 0.8488 Sum Loss: 2.2220
Epoch 2/2... Discriminator Loss: 1.3619... Generator Loss: 0.6912 Sum Loss: 2.0531
Epoch 2/2... Discriminator Loss: 1.4557... Generator Loss: 0.5569 Sum Loss: 2.0126
Epoch 2/2... Discriminator Loss: 1.4575... Generator Loss: 0.5460 Sum Loss: 2.0035
Epoch 2/2... Discriminator Loss: 1.4134... Generator Loss: 0.7177 Sum Loss: 2.1311
Epoch 2/2... Discriminator Loss: 1.3256... Generator Loss: 0.5810 Sum Loss: 1.9066
Epoch 2/2... Discriminator Loss: 1.4394... Generator Loss: 0.7346 Sum Loss: 2.1741
Epoch 2/2... Discriminator Loss: 1.4058... Generator Loss: 0.5695 Sum Loss: 1.9752
Epoch 2/2... Discriminator Loss: 1.4509... Generator Loss: 0.5691 Sum Loss: 2.0200
Epoch 2/2... Discriminator Loss: 1.4071... Generator Loss: 0.8451 Sum Loss: 2.2522
Epoch 2/2... Discriminator Loss: 1.4319... Generator Loss: 0.6463 Sum Loss: 2.0782
Epoch 2/2... Discriminator Loss: 1.3525... Generator Loss: 0.6290 Sum Loss: 1.9815
Epoch 2/2... Discriminator Loss: 1.3531... Generator Loss: 0.7028 Sum Loss: 2.0559
Epoch 2/2... Discriminator Loss: 1.4362... Generator Loss: 0.6974 Sum Loss: 2.1336
Epoch 2/2... Discriminator Loss: 1.4121... Generator Loss: 0.6679 Sum Loss: 2.0800
Epoch 2/2... Discriminator Loss: 1.4492... Generator Loss: 0.8955 Sum Loss: 2.3447
Epoch 2/2... Discriminator Loss: 1.3596... Generator Loss: 0.5236 Sum Loss: 1.8831
Epoch 2/2... Discriminator Loss: 1.4679... Generator Loss: 0.5230 Sum Loss: 1.9909
Epoch 2/2... Discriminator Loss: 1.3253... Generator Loss: 0.7085 Sum Loss: 2.0338
Epoch 2/2... Discriminator Loss: 1.4064... Generator Loss: 0.6712 Sum Loss: 2.0776
Epoch 2/2... Discriminator Loss: 1.4248... Generator Loss: 0.5611 Sum Loss: 1.9858
Epoch 2/2... Discriminator Loss: 1.3680... Generator Loss: 0.7267 Sum Loss: 2.0947
Epoch 2/2... Discriminator Loss: 1.4165... Generator Loss: 0.6293 Sum Loss: 2.0458
Epoch 2/2... Discriminator Loss: 1.5121... Generator Loss: 0.5233 Sum Loss: 2.0355
Epoch 2/2... Discriminator Loss: 1.3716... Generator Loss: 0.8161 Sum Loss: 2.1877
Epoch 2/2... Discriminator Loss: 1.4475... Generator Loss: 0.6927 Sum Loss: 2.1401
Epoch 2/2... Discriminator Loss: 1.4186... Generator Loss: 0.8669 Sum Loss: 2.2856
Epoch 2/2... Discriminator Loss: 1.3671... Generator Loss: 0.6797 Sum Loss: 2.0468
Epoch 2/2... Discriminator Loss: 1.3950... Generator Loss: 0.6186 Sum Loss: 2.0136
Epoch 2/2... Discriminator Loss: 1.4812... Generator Loss: 0.5741 Sum Loss: 2.0553
Epoch 2/2... Discriminator Loss: 1.3393... Generator Loss: 0.6439 Sum Loss: 1.9833
Epoch 2/2... Discriminator Loss: 1.3864... Generator Loss: 0.5917 Sum Loss: 1.9781
Epoch 2/2... Discriminator Loss: 1.3892... Generator Loss: 0.6380 Sum Loss: 2.0272
Epoch 2/2... Discriminator Loss: 1.3803... Generator Loss: 0.7212 Sum Loss: 2.1015
Epoch 2/2... Discriminator Loss: 1.4363... Generator Loss: 0.9859 Sum Loss: 2.4222
Epoch 2/2... Discriminator Loss: 1.3448... Generator Loss: 0.7296 Sum Loss: 2.0744
Epoch 2/2... Discriminator Loss: 1.4797... Generator Loss: 0.7538 Sum Loss: 2.2335
Epoch 2/2... Discriminator Loss: 1.3988... Generator Loss: 0.6943 Sum Loss: 2.0932
Epoch 2/2... Discriminator Loss: 1.3863... Generator Loss: 0.6951 Sum Loss: 2.0814
Epoch 2/2... Discriminator Loss: 1.3987... Generator Loss: 0.6902 Sum Loss: 2.0889
Epoch 2/2... Discriminator Loss: 1.4309... Generator Loss: 0.7114 Sum Loss: 2.1422
Epoch 2/2... Discriminator Loss: 1.4229... Generator Loss: 0.6207 Sum Loss: 2.0436
Epoch 2/2... Discriminator Loss: 1.4727... Generator Loss: 0.5330 Sum Loss: 2.0058
Epoch 2/2... Discriminator Loss: 1.3864... Generator Loss: 0.5716 Sum Loss: 1.9580
Epoch 2/2... Discriminator Loss: 1.4907... Generator Loss: 0.5114 Sum Loss: 2.0021
Epoch 2/2... Discriminator Loss: 1.3593... Generator Loss: 0.8203 Sum Loss: 2.1796
Epoch 2/2... Discriminator Loss: 1.5158... Generator Loss: 0.5108 Sum Loss: 2.0266
Epoch 2/2... Discriminator Loss: 1.3879... Generator Loss: 0.8093 Sum Loss: 2.1972
Epoch 2/2... Discriminator Loss: 1.3598... Generator Loss: 0.6797 Sum Loss: 2.0394
Epoch 2/2... Discriminator Loss: 1.4489... Generator Loss: 0.6986 Sum Loss: 2.1475
Epoch 2/2... Discriminator Loss: 1.3989... Generator Loss: 0.7444 Sum Loss: 2.1433
Epoch 2/2... Discriminator Loss: 1.3198... Generator Loss: 0.7077 Sum Loss: 2.0274
Epoch 2/2... Discriminator Loss: 1.3940... Generator Loss: 0.6713 Sum Loss: 2.0653
Epoch 2/2... Discriminator Loss: 1.3681... Generator Loss: 0.6671 Sum Loss: 2.0352
Epoch 2/2... Discriminator Loss: 1.4711... Generator Loss: 0.9380 Sum Loss: 2.4092
Epoch 2/2... Discriminator Loss: 1.4097... Generator Loss: 0.8855 Sum Loss: 2.2952
Epoch 2/2... Discriminator Loss: 1.4063... Generator Loss: 0.6872 Sum Loss: 2.0935
Epoch 2/2... Discriminator Loss: 1.3874... Generator Loss: 0.6313 Sum Loss: 2.0186
Epoch 2/2... Discriminator Loss: 1.5495... Generator Loss: 0.4615 Sum Loss: 2.0110
Epoch 2/2... Discriminator Loss: 1.3772... Generator Loss: 0.7287 Sum Loss: 2.1059
Epoch 2/2... Discriminator Loss: 1.3995... Generator Loss: 0.5472 Sum Loss: 1.9467
Epoch 2/2... Discriminator Loss: 1.3730... Generator Loss: 0.7240 Sum Loss: 2.0970
Epoch 2/2... Discriminator Loss: 1.3703... Generator Loss: 0.8370 Sum Loss: 2.2073
Epoch 2/2... Discriminator Loss: 1.4439... Generator Loss: 0.5058 Sum Loss: 1.9497
Epoch 2/2... Discriminator Loss: 1.4942... Generator Loss: 0.8462 Sum Loss: 2.3404

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [37]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 4.3116... Generator Loss: 0.0174 Sum Loss: 4.3289
Epoch 1/1... Discriminator Loss: 5.0307... Generator Loss: 0.0116 Sum Loss: 5.0423
Epoch 1/1... Discriminator Loss: 5.1111... Generator Loss: 0.0111 Sum Loss: 5.1222
Epoch 1/1... Discriminator Loss: 3.7735... Generator Loss: 0.0570 Sum Loss: 3.8305
Epoch 1/1... Discriminator Loss: 3.5760... Generator Loss: 0.0717 Sum Loss: 3.6477
Epoch 1/1... Discriminator Loss: 3.4050... Generator Loss: 0.1082 Sum Loss: 3.5133
Epoch 1/1... Discriminator Loss: 3.0410... Generator Loss: 0.1706 Sum Loss: 3.2117
Epoch 1/1... Discriminator Loss: 3.2055... Generator Loss: 0.0965 Sum Loss: 3.3020
Epoch 1/1... Discriminator Loss: 2.7553... Generator Loss: 0.1577 Sum Loss: 2.9130
Epoch 1/1... Discriminator Loss: 2.3337... Generator Loss: 0.1944 Sum Loss: 2.5281
Epoch 1/1... Discriminator Loss: 1.0201... Generator Loss: 1.3243 Sum Loss: 2.3444
Epoch 1/1... Discriminator Loss: 2.3291... Generator Loss: 0.2779 Sum Loss: 2.6070
Epoch 1/1... Discriminator Loss: 2.1389... Generator Loss: 0.3318 Sum Loss: 2.4708
Epoch 1/1... Discriminator Loss: 2.2574... Generator Loss: 0.4536 Sum Loss: 2.7110
Epoch 1/1... Discriminator Loss: 2.3041... Generator Loss: 0.5031 Sum Loss: 2.8073
Epoch 1/1... Discriminator Loss: 2.2169... Generator Loss: 0.4307 Sum Loss: 2.6476
Epoch 1/1... Discriminator Loss: 2.0267... Generator Loss: 0.5459 Sum Loss: 2.5726
Epoch 1/1... Discriminator Loss: 2.1923... Generator Loss: 0.5457 Sum Loss: 2.7380
Epoch 1/1... Discriminator Loss: 2.0161... Generator Loss: 0.4526 Sum Loss: 2.4687
Epoch 1/1... Discriminator Loss: 1.8977... Generator Loss: 0.4315 Sum Loss: 2.3292
Epoch 1/1... Discriminator Loss: 1.9140... Generator Loss: 0.5682 Sum Loss: 2.4822
Epoch 1/1... Discriminator Loss: 1.8526... Generator Loss: 0.5126 Sum Loss: 2.3652
Epoch 1/1... Discriminator Loss: 1.8675... Generator Loss: 0.4726 Sum Loss: 2.3401
Epoch 1/1... Discriminator Loss: 1.9937... Generator Loss: 0.6517 Sum Loss: 2.6455
Epoch 1/1... Discriminator Loss: 1.7932... Generator Loss: 0.5678 Sum Loss: 2.3609
Epoch 1/1... Discriminator Loss: 1.8214... Generator Loss: 0.5222 Sum Loss: 2.3436
Epoch 1/1... Discriminator Loss: 1.9026... Generator Loss: 0.4308 Sum Loss: 2.3335
Epoch 1/1... Discriminator Loss: 1.8064... Generator Loss: 0.5631 Sum Loss: 2.3695
Epoch 1/1... Discriminator Loss: 1.8025... Generator Loss: 0.5036 Sum Loss: 2.3061
Epoch 1/1... Discriminator Loss: 1.6536... Generator Loss: 0.5489 Sum Loss: 2.2026
Epoch 1/1... Discriminator Loss: 1.7669... Generator Loss: 0.4296 Sum Loss: 2.1964
Epoch 1/1... Discriminator Loss: 1.6953... Generator Loss: 0.5037 Sum Loss: 2.1989
Epoch 1/1... Discriminator Loss: 1.6330... Generator Loss: 0.7087 Sum Loss: 2.3416
Epoch 1/1... Discriminator Loss: 1.6882... Generator Loss: 0.5539 Sum Loss: 2.2421
Epoch 1/1... Discriminator Loss: 1.7414... Generator Loss: 0.5284 Sum Loss: 2.2698
Epoch 1/1... Discriminator Loss: 1.6401... Generator Loss: 0.6210 Sum Loss: 2.2610
Epoch 1/1... Discriminator Loss: 1.6571... Generator Loss: 0.6177 Sum Loss: 2.2749
Epoch 1/1... Discriminator Loss: 1.6593... Generator Loss: 0.5063 Sum Loss: 2.1656
Epoch 1/1... Discriminator Loss: 1.5999... Generator Loss: 0.5850 Sum Loss: 2.1849
Epoch 1/1... Discriminator Loss: 1.5588... Generator Loss: 0.5642 Sum Loss: 2.1230
Epoch 1/1... Discriminator Loss: 1.5919... Generator Loss: 0.7187 Sum Loss: 2.3106
Epoch 1/1... Discriminator Loss: 1.5156... Generator Loss: 0.5623 Sum Loss: 2.0780
Epoch 1/1... Discriminator Loss: 1.5543... Generator Loss: 0.6176 Sum Loss: 2.1720
Epoch 1/1... Discriminator Loss: 1.6196... Generator Loss: 0.6393 Sum Loss: 2.2589
Epoch 1/1... Discriminator Loss: 1.6196... Generator Loss: 0.6259 Sum Loss: 2.2455
Epoch 1/1... Discriminator Loss: 1.5188... Generator Loss: 0.5717 Sum Loss: 2.0904
Epoch 1/1... Discriminator Loss: 1.5641... Generator Loss: 0.5526 Sum Loss: 2.1167
Epoch 1/1... Discriminator Loss: 1.5525... Generator Loss: 0.6033 Sum Loss: 2.1558
Epoch 1/1... Discriminator Loss: 1.6389... Generator Loss: 0.7795 Sum Loss: 2.4184
Epoch 1/1... Discriminator Loss: 1.6004... Generator Loss: 0.5267 Sum Loss: 2.1271
Epoch 1/1... Discriminator Loss: 1.5806... Generator Loss: 0.6322 Sum Loss: 2.2127
Epoch 1/1... Discriminator Loss: 1.5621... Generator Loss: 0.5854 Sum Loss: 2.1474
Epoch 1/1... Discriminator Loss: 1.5892... Generator Loss: 0.7232 Sum Loss: 2.3124
Epoch 1/1... Discriminator Loss: 1.4755... Generator Loss: 0.8914 Sum Loss: 2.3670
Epoch 1/1... Discriminator Loss: 1.4567... Generator Loss: 0.6041 Sum Loss: 2.0609
Epoch 1/1... Discriminator Loss: 1.4945... Generator Loss: 0.5798 Sum Loss: 2.0743
Epoch 1/1... Discriminator Loss: 1.5209... Generator Loss: 0.7814 Sum Loss: 2.3023
Epoch 1/1... Discriminator Loss: 1.4826... Generator Loss: 0.8122 Sum Loss: 2.2948
Epoch 1/1... Discriminator Loss: 1.4519... Generator Loss: 0.6307 Sum Loss: 2.0825
Epoch 1/1... Discriminator Loss: 1.4890... Generator Loss: 0.6416 Sum Loss: 2.1305
Epoch 1/1... Discriminator Loss: 1.4777... Generator Loss: 0.6956 Sum Loss: 2.1733
Epoch 1/1... Discriminator Loss: 1.4939... Generator Loss: 0.7764 Sum Loss: 2.2703
Epoch 1/1... Discriminator Loss: 1.2816... Generator Loss: 0.7731 Sum Loss: 2.0547
Epoch 1/1... Discriminator Loss: 1.4512... Generator Loss: 0.6284 Sum Loss: 2.0796
Epoch 1/1... Discriminator Loss: 1.5354... Generator Loss: 0.6552 Sum Loss: 2.1906
Epoch 1/1... Discriminator Loss: 1.2669... Generator Loss: 0.6855 Sum Loss: 1.9524
Epoch 1/1... Discriminator Loss: 1.4804... Generator Loss: 0.7424 Sum Loss: 2.2227
Epoch 1/1... Discriminator Loss: 1.4388... Generator Loss: 0.6641 Sum Loss: 2.1029
Epoch 1/1... Discriminator Loss: 1.4564... Generator Loss: 0.5774 Sum Loss: 2.0337
Epoch 1/1... Discriminator Loss: 1.4503... Generator Loss: 0.6801 Sum Loss: 2.1305
Epoch 1/1... Discriminator Loss: 1.4756... Generator Loss: 0.7568 Sum Loss: 2.2324
Epoch 1/1... Discriminator Loss: 1.5751... Generator Loss: 0.6713 Sum Loss: 2.2464
Epoch 1/1... Discriminator Loss: 1.5056... Generator Loss: 0.4821 Sum Loss: 1.9877
Epoch 1/1... Discriminator Loss: 1.4120... Generator Loss: 0.8317 Sum Loss: 2.2438
Epoch 1/1... Discriminator Loss: 1.4554... Generator Loss: 0.6465 Sum Loss: 2.1018
Epoch 1/1... Discriminator Loss: 1.4130... Generator Loss: 0.5910 Sum Loss: 2.0040
Epoch 1/1... Discriminator Loss: 1.3988... Generator Loss: 0.8768 Sum Loss: 2.2756
Epoch 1/1... Discriminator Loss: 1.3422... Generator Loss: 0.8366 Sum Loss: 2.1789
Epoch 1/1... Discriminator Loss: 1.3996... Generator Loss: 0.6198 Sum Loss: 2.0194
Epoch 1/1... Discriminator Loss: 1.3956... Generator Loss: 0.6426 Sum Loss: 2.0382
Epoch 1/1... Discriminator Loss: 1.3304... Generator Loss: 0.6519 Sum Loss: 1.9823
Epoch 1/1... Discriminator Loss: 1.4018... Generator Loss: 0.5313 Sum Loss: 1.9331
Epoch 1/1... Discriminator Loss: 1.3828... Generator Loss: 0.7305 Sum Loss: 2.1133
Epoch 1/1... Discriminator Loss: 1.3363... Generator Loss: 0.6999 Sum Loss: 2.0362
Epoch 1/1... Discriminator Loss: 1.2735... Generator Loss: 0.7415 Sum Loss: 2.0150
Epoch 1/1... Discriminator Loss: 1.1135... Generator Loss: 1.1082 Sum Loss: 2.2217
Epoch 1/1... Discriminator Loss: 0.9736... Generator Loss: 1.1150 Sum Loss: 2.0886
Epoch 1/1... Discriminator Loss: 1.2875... Generator Loss: 0.5672 Sum Loss: 1.8547
Epoch 1/1... Discriminator Loss: 0.8725... Generator Loss: 1.1339 Sum Loss: 2.0064
Epoch 1/1... Discriminator Loss: 0.9418... Generator Loss: 1.0931 Sum Loss: 2.0349
Epoch 1/1... Discriminator Loss: 0.9638... Generator Loss: 1.1646 Sum Loss: 2.1284
Epoch 1/1... Discriminator Loss: 0.9166... Generator Loss: 1.3602 Sum Loss: 2.2767
Epoch 1/1... Discriminator Loss: 1.0099... Generator Loss: 1.4321 Sum Loss: 2.4420
Epoch 1/1... Discriminator Loss: 0.8344... Generator Loss: 1.4972 Sum Loss: 2.3317
Epoch 1/1... Discriminator Loss: 0.8774... Generator Loss: 1.5056 Sum Loss: 2.3830
Epoch 1/1... Discriminator Loss: 0.8091... Generator Loss: 1.1977 Sum Loss: 2.0068
Epoch 1/1... Discriminator Loss: 0.9499... Generator Loss: 0.9130 Sum Loss: 1.8628
Epoch 1/1... Discriminator Loss: 0.6694... Generator Loss: 2.2852 Sum Loss: 2.9546
Epoch 1/1... Discriminator Loss: 0.7675... Generator Loss: 1.7490 Sum Loss: 2.5165
Epoch 1/1... Discriminator Loss: 1.1462... Generator Loss: 0.5188 Sum Loss: 1.6650
Epoch 1/1... Discriminator Loss: 0.9878... Generator Loss: 1.0773 Sum Loss: 2.0651
Epoch 1/1... Discriminator Loss: 0.6169... Generator Loss: 1.5414 Sum Loss: 2.1582
Epoch 1/1... Discriminator Loss: 0.8435... Generator Loss: 1.4777 Sum Loss: 2.3212
Epoch 1/1... Discriminator Loss: 0.4706... Generator Loss: 1.2453 Sum Loss: 1.7159
Epoch 1/1... Discriminator Loss: 1.4690... Generator Loss: 0.4135 Sum Loss: 1.8824
Epoch 1/1... Discriminator Loss: 0.8468... Generator Loss: 1.3480 Sum Loss: 2.1948
Epoch 1/1... Discriminator Loss: 0.9215... Generator Loss: 1.6574 Sum Loss: 2.5789
Epoch 1/1... Discriminator Loss: 1.2996... Generator Loss: 0.6289 Sum Loss: 1.9285
Epoch 1/1... Discriminator Loss: 1.4003... Generator Loss: 0.5621 Sum Loss: 1.9625
Epoch 1/1... Discriminator Loss: 1.1782... Generator Loss: 0.7456 Sum Loss: 1.9238
Epoch 1/1... Discriminator Loss: 0.9380... Generator Loss: 1.0890 Sum Loss: 2.0270
Epoch 1/1... Discriminator Loss: 1.5241... Generator Loss: 0.3714 Sum Loss: 1.8955
Epoch 1/1... Discriminator Loss: 1.2526... Generator Loss: 0.9219 Sum Loss: 2.1745
Epoch 1/1... Discriminator Loss: 1.3457... Generator Loss: 0.7696 Sum Loss: 2.1153
Epoch 1/1... Discriminator Loss: 0.9254... Generator Loss: 0.8598 Sum Loss: 1.7852
Epoch 1/1... Discriminator Loss: 1.3697... Generator Loss: 0.7708 Sum Loss: 2.1405
Epoch 1/1... Discriminator Loss: 0.9878... Generator Loss: 1.2998 Sum Loss: 2.2877
Epoch 1/1... Discriminator Loss: 1.2225... Generator Loss: 0.6670 Sum Loss: 1.8894
Epoch 1/1... Discriminator Loss: 0.9365... Generator Loss: 0.8727 Sum Loss: 1.8092
Epoch 1/1... Discriminator Loss: 1.3203... Generator Loss: 0.6766 Sum Loss: 1.9969
Epoch 1/1... Discriminator Loss: 0.7844... Generator Loss: 1.3012 Sum Loss: 2.0857
Epoch 1/1... Discriminator Loss: 1.3364... Generator Loss: 0.8368 Sum Loss: 2.1732
Epoch 1/1... Discriminator Loss: 1.1339... Generator Loss: 1.2155 Sum Loss: 2.3493
Epoch 1/1... Discriminator Loss: 1.5025... Generator Loss: 0.9067 Sum Loss: 2.4092
Epoch 1/1... Discriminator Loss: 1.2067... Generator Loss: 0.7878 Sum Loss: 1.9945
Epoch 1/1... Discriminator Loss: 1.4530... Generator Loss: 0.6991 Sum Loss: 2.1521
Epoch 1/1... Discriminator Loss: 1.2730... Generator Loss: 0.5736 Sum Loss: 1.8467
Epoch 1/1... Discriminator Loss: 1.1724... Generator Loss: 0.9437 Sum Loss: 2.1161
Epoch 1/1... Discriminator Loss: 2.1466... Generator Loss: 1.3385 Sum Loss: 3.4850
Epoch 1/1... Discriminator Loss: 1.2247... Generator Loss: 1.5574 Sum Loss: 2.7822
Epoch 1/1... Discriminator Loss: 1.0177... Generator Loss: 2.3661 Sum Loss: 3.3839
Epoch 1/1... Discriminator Loss: 0.7381... Generator Loss: 1.5611 Sum Loss: 2.2992
Epoch 1/1... Discriminator Loss: 2.0579... Generator Loss: 0.2286 Sum Loss: 2.2865
Epoch 1/1... Discriminator Loss: 1.1878... Generator Loss: 0.7584 Sum Loss: 1.9462
Epoch 1/1... Discriminator Loss: 1.2761... Generator Loss: 0.5376 Sum Loss: 1.8137
Epoch 1/1... Discriminator Loss: 1.2090... Generator Loss: 0.6869 Sum Loss: 1.8959
Epoch 1/1... Discriminator Loss: 1.1548... Generator Loss: 0.8751 Sum Loss: 2.0299
Epoch 1/1... Discriminator Loss: 1.1205... Generator Loss: 0.9852 Sum Loss: 2.1058
Epoch 1/1... Discriminator Loss: 1.2688... Generator Loss: 0.9076 Sum Loss: 2.1764
Epoch 1/1... Discriminator Loss: 1.2725... Generator Loss: 0.9563 Sum Loss: 2.2288
Epoch 1/1... Discriminator Loss: 1.1543... Generator Loss: 0.6264 Sum Loss: 1.7808
Epoch 1/1... Discriminator Loss: 1.3440... Generator Loss: 0.6118 Sum Loss: 1.9557
Epoch 1/1... Discriminator Loss: 1.4847... Generator Loss: 0.6591 Sum Loss: 2.1438
Epoch 1/1... Discriminator Loss: 1.4834... Generator Loss: 0.4895 Sum Loss: 1.9729
Epoch 1/1... Discriminator Loss: 1.2762... Generator Loss: 0.7409 Sum Loss: 2.0172
Epoch 1/1... Discriminator Loss: 1.0506... Generator Loss: 0.9642 Sum Loss: 2.0148
Epoch 1/1... Discriminator Loss: 1.4412... Generator Loss: 0.7960 Sum Loss: 2.2373
Epoch 1/1... Discriminator Loss: 1.1321... Generator Loss: 0.8743 Sum Loss: 2.0064
Epoch 1/1... Discriminator Loss: 1.7311... Generator Loss: 0.3792 Sum Loss: 2.1103
Epoch 1/1... Discriminator Loss: 1.7484... Generator Loss: 0.7915 Sum Loss: 2.5398
Epoch 1/1... Discriminator Loss: 1.5167... Generator Loss: 0.6749 Sum Loss: 2.1916
Epoch 1/1... Discriminator Loss: 1.0402... Generator Loss: 1.3007 Sum Loss: 2.3409
Epoch 1/1... Discriminator Loss: 1.6402... Generator Loss: 0.5892 Sum Loss: 2.2294
Epoch 1/1... Discriminator Loss: 1.6559... Generator Loss: 0.5665 Sum Loss: 2.2225
Epoch 1/1... Discriminator Loss: 1.3214... Generator Loss: 0.8165 Sum Loss: 2.1379
Epoch 1/1... Discriminator Loss: 1.5135... Generator Loss: 0.5890 Sum Loss: 2.1026
Epoch 1/1... Discriminator Loss: 1.4497... Generator Loss: 0.4795 Sum Loss: 1.9292
Epoch 1/1... Discriminator Loss: 1.4095... Generator Loss: 0.6628 Sum Loss: 2.0722
Epoch 1/1... Discriminator Loss: 1.3798... Generator Loss: 0.6766 Sum Loss: 2.0564
Epoch 1/1... Discriminator Loss: 1.5566... Generator Loss: 0.5551 Sum Loss: 2.1117
Epoch 1/1... Discriminator Loss: 0.9867... Generator Loss: 1.3018 Sum Loss: 2.2886
Epoch 1/1... Discriminator Loss: 1.0522... Generator Loss: 0.8188 Sum Loss: 1.8709
Epoch 1/1... Discriminator Loss: 1.2728... Generator Loss: 0.6252 Sum Loss: 1.8979
Epoch 1/1... Discriminator Loss: 1.2951... Generator Loss: 0.7645 Sum Loss: 2.0596
Epoch 1/1... Discriminator Loss: 1.7421... Generator Loss: 0.5954 Sum Loss: 2.3375
Epoch 1/1... Discriminator Loss: 1.6249... Generator Loss: 0.5107 Sum Loss: 2.1356
Epoch 1/1... Discriminator Loss: 1.5629... Generator Loss: 0.6462 Sum Loss: 2.2091
Epoch 1/1... Discriminator Loss: 1.3382... Generator Loss: 0.6740 Sum Loss: 2.0121
Epoch 1/1... Discriminator Loss: 1.3409... Generator Loss: 0.8352 Sum Loss: 2.1761
Epoch 1/1... Discriminator Loss: 1.4197... Generator Loss: 0.8585 Sum Loss: 2.2783
Epoch 1/1... Discriminator Loss: 1.5716... Generator Loss: 0.8102 Sum Loss: 2.3819
Epoch 1/1... Discriminator Loss: 1.5044... Generator Loss: 0.5424 Sum Loss: 2.0468
Epoch 1/1... Discriminator Loss: 1.3057... Generator Loss: 0.6643 Sum Loss: 1.9700
Epoch 1/1... Discriminator Loss: 1.4107... Generator Loss: 0.8168 Sum Loss: 2.2275
Epoch 1/1... Discriminator Loss: 1.3630... Generator Loss: 0.7156 Sum Loss: 2.0786
Epoch 1/1... Discriminator Loss: 1.4225... Generator Loss: 0.6361 Sum Loss: 2.0586
Epoch 1/1... Discriminator Loss: 1.3141... Generator Loss: 0.8089 Sum Loss: 2.1231
Epoch 1/1... Discriminator Loss: 1.5527... Generator Loss: 0.4984 Sum Loss: 2.0511
Epoch 1/1... Discriminator Loss: 1.1600... Generator Loss: 1.0345 Sum Loss: 2.1944
Epoch 1/1... Discriminator Loss: 1.4339... Generator Loss: 0.4647 Sum Loss: 1.8987
Epoch 1/1... Discriminator Loss: 1.3784... Generator Loss: 0.6730 Sum Loss: 2.0515
Epoch 1/1... Discriminator Loss: 1.5334... Generator Loss: 0.4376 Sum Loss: 1.9710
Epoch 1/1... Discriminator Loss: 1.3924... Generator Loss: 0.6960 Sum Loss: 2.0884
Epoch 1/1... Discriminator Loss: 1.4619... Generator Loss: 0.6248 Sum Loss: 2.0867
Epoch 1/1... Discriminator Loss: 1.2871... Generator Loss: 0.9464 Sum Loss: 2.2335
Epoch 1/1... Discriminator Loss: 1.7798... Generator Loss: 0.4970 Sum Loss: 2.2768
Epoch 1/1... Discriminator Loss: 1.3870... Generator Loss: 0.6696 Sum Loss: 2.0567
Epoch 1/1... Discriminator Loss: 1.3843... Generator Loss: 0.7782 Sum Loss: 2.1625
Epoch 1/1... Discriminator Loss: 1.3512... Generator Loss: 0.7423 Sum Loss: 2.0935
Epoch 1/1... Discriminator Loss: 1.2461... Generator Loss: 0.7858 Sum Loss: 2.0319
Epoch 1/1... Discriminator Loss: 1.6151... Generator Loss: 0.5728 Sum Loss: 2.1879
Epoch 1/1... Discriminator Loss: 1.5028... Generator Loss: 0.5602 Sum Loss: 2.0630
Epoch 1/1... Discriminator Loss: 1.4965... Generator Loss: 0.7797 Sum Loss: 2.2763
Epoch 1/1... Discriminator Loss: 1.6439... Generator Loss: 0.6086 Sum Loss: 2.2525
Epoch 1/1... Discriminator Loss: 1.4015... Generator Loss: 0.6803 Sum Loss: 2.0818
Epoch 1/1... Discriminator Loss: 1.5941... Generator Loss: 0.6019 Sum Loss: 2.1961
Epoch 1/1... Discriminator Loss: 1.5198... Generator Loss: 0.6431 Sum Loss: 2.1629
Epoch 1/1... Discriminator Loss: 1.4215... Generator Loss: 0.7341 Sum Loss: 2.1556
Epoch 1/1... Discriminator Loss: 1.5112... Generator Loss: 0.7082 Sum Loss: 2.2194
Epoch 1/1... Discriminator Loss: 1.5002... Generator Loss: 0.5954 Sum Loss: 2.0956
Epoch 1/1... Discriminator Loss: 1.4639... Generator Loss: 0.6161 Sum Loss: 2.0800
Epoch 1/1... Discriminator Loss: 1.4566... Generator Loss: 0.7490 Sum Loss: 2.2057
Epoch 1/1... Discriminator Loss: 1.5474... Generator Loss: 0.4823 Sum Loss: 2.0297
Epoch 1/1... Discriminator Loss: 1.3481... Generator Loss: 0.7042 Sum Loss: 2.0523
Epoch 1/1... Discriminator Loss: 1.3611... Generator Loss: 0.6889 Sum Loss: 2.0500
Epoch 1/1... Discriminator Loss: 1.3077... Generator Loss: 0.8539 Sum Loss: 2.1616
Epoch 1/1... Discriminator Loss: 1.4409... Generator Loss: 0.7274 Sum Loss: 2.1684
Epoch 1/1... Discriminator Loss: 1.4322... Generator Loss: 0.5368 Sum Loss: 1.9690
Epoch 1/1... Discriminator Loss: 1.3882... Generator Loss: 0.6684 Sum Loss: 2.0566
Epoch 1/1... Discriminator Loss: 1.4173... Generator Loss: 0.5017 Sum Loss: 1.9190
Epoch 1/1... Discriminator Loss: 1.4443... Generator Loss: 0.5116 Sum Loss: 1.9560
Epoch 1/1... Discriminator Loss: 1.5598... Generator Loss: 0.4898 Sum Loss: 2.0496
Epoch 1/1... Discriminator Loss: 1.4640... Generator Loss: 0.4975 Sum Loss: 1.9615
Epoch 1/1... Discriminator Loss: 1.4568... Generator Loss: 0.5858 Sum Loss: 2.0426
Epoch 1/1... Discriminator Loss: 1.5347... Generator Loss: 0.4761 Sum Loss: 2.0109
Epoch 1/1... Discriminator Loss: 1.4201... Generator Loss: 0.7853 Sum Loss: 2.2054
Epoch 1/1... Discriminator Loss: 1.4971... Generator Loss: 0.5519 Sum Loss: 2.0490
Epoch 1/1... Discriminator Loss: 1.4720... Generator Loss: 0.5807 Sum Loss: 2.0527
Epoch 1/1... Discriminator Loss: 1.3247... Generator Loss: 0.5534 Sum Loss: 1.8781
Epoch 1/1... Discriminator Loss: 1.3859... Generator Loss: 0.5419 Sum Loss: 1.9278
Epoch 1/1... Discriminator Loss: 1.3343... Generator Loss: 0.8763 Sum Loss: 2.2105
Epoch 1/1... Discriminator Loss: 1.3962... Generator Loss: 0.7105 Sum Loss: 2.1068
Epoch 1/1... Discriminator Loss: 1.4186... Generator Loss: 0.7721 Sum Loss: 2.1906
Epoch 1/1... Discriminator Loss: 1.5119... Generator Loss: 0.5445 Sum Loss: 2.0563
Epoch 1/1... Discriminator Loss: 1.4923... Generator Loss: 0.6854 Sum Loss: 2.1777
Epoch 1/1... Discriminator Loss: 1.4677... Generator Loss: 0.6995 Sum Loss: 2.1672
Epoch 1/1... Discriminator Loss: 1.4254... Generator Loss: 0.6663 Sum Loss: 2.0917
Epoch 1/1... Discriminator Loss: 1.4979... Generator Loss: 0.5687 Sum Loss: 2.0665
Epoch 1/1... Discriminator Loss: 1.4005... Generator Loss: 0.7416 Sum Loss: 2.1420
Epoch 1/1... Discriminator Loss: 1.4045... Generator Loss: 0.5973 Sum Loss: 2.0018
Epoch 1/1... Discriminator Loss: 1.5309... Generator Loss: 0.5398 Sum Loss: 2.0707
Epoch 1/1... Discriminator Loss: 1.5049... Generator Loss: 0.5617 Sum Loss: 2.0666
Epoch 1/1... Discriminator Loss: 1.5512... Generator Loss: 0.5408 Sum Loss: 2.0920
Epoch 1/1... Discriminator Loss: 1.5014... Generator Loss: 0.5623 Sum Loss: 2.0637
Epoch 1/1... Discriminator Loss: 1.3417... Generator Loss: 0.8354 Sum Loss: 2.1772
Epoch 1/1... Discriminator Loss: 1.3833... Generator Loss: 0.7144 Sum Loss: 2.0978
Epoch 1/1... Discriminator Loss: 1.3439... Generator Loss: 0.8384 Sum Loss: 2.1823
Epoch 1/1... Discriminator Loss: 1.4174... Generator Loss: 0.5704 Sum Loss: 1.9877
Epoch 1/1... Discriminator Loss: 1.3921... Generator Loss: 0.7137 Sum Loss: 2.1057
Epoch 1/1... Discriminator Loss: 1.3034... Generator Loss: 0.7482 Sum Loss: 2.0516
Epoch 1/1... Discriminator Loss: 1.3338... Generator Loss: 0.6787 Sum Loss: 2.0125
Epoch 1/1... Discriminator Loss: 1.4126... Generator Loss: 0.6227 Sum Loss: 2.0353
Epoch 1/1... Discriminator Loss: 1.3961... Generator Loss: 0.7005 Sum Loss: 2.0966
Epoch 1/1... Discriminator Loss: 1.3242... Generator Loss: 0.8510 Sum Loss: 2.1752
Epoch 1/1... Discriminator Loss: 1.4645... Generator Loss: 0.5688 Sum Loss: 2.0333
Epoch 1/1... Discriminator Loss: 1.3277... Generator Loss: 0.8530 Sum Loss: 2.1808
Epoch 1/1... Discriminator Loss: 1.4279... Generator Loss: 0.6615 Sum Loss: 2.0894
Epoch 1/1... Discriminator Loss: 1.3533... Generator Loss: 0.7237 Sum Loss: 2.0770
Epoch 1/1... Discriminator Loss: 1.4543... Generator Loss: 0.5853 Sum Loss: 2.0396
Epoch 1/1... Discriminator Loss: 1.3169... Generator Loss: 0.9197 Sum Loss: 2.2366
Epoch 1/1... Discriminator Loss: 1.5010... Generator Loss: 0.3853 Sum Loss: 1.8863
Epoch 1/1... Discriminator Loss: 1.5474... Generator Loss: 0.4520 Sum Loss: 1.9994
Epoch 1/1... Discriminator Loss: 1.5301... Generator Loss: 0.6042 Sum Loss: 2.1343
Epoch 1/1... Discriminator Loss: 1.3071... Generator Loss: 0.6461 Sum Loss: 1.9532
Epoch 1/1... Discriminator Loss: 1.3002... Generator Loss: 0.6854 Sum Loss: 1.9857
Epoch 1/1... Discriminator Loss: 1.2643... Generator Loss: 0.7785 Sum Loss: 2.0429
Epoch 1/1... Discriminator Loss: 1.2583... Generator Loss: 0.7638 Sum Loss: 2.0220
Epoch 1/1... Discriminator Loss: 1.4786... Generator Loss: 0.5852 Sum Loss: 2.0639
Epoch 1/1... Discriminator Loss: 1.4668... Generator Loss: 0.5494 Sum Loss: 2.0162
Epoch 1/1... Discriminator Loss: 1.4012... Generator Loss: 0.6529 Sum Loss: 2.0541
Epoch 1/1... Discriminator Loss: 1.2617... Generator Loss: 0.7231 Sum Loss: 1.9848
Epoch 1/1... Discriminator Loss: 1.4397... Generator Loss: 0.6150 Sum Loss: 2.0547
Epoch 1/1... Discriminator Loss: 1.4321... Generator Loss: 0.7021 Sum Loss: 2.1341
Epoch 1/1... Discriminator Loss: 1.4818... Generator Loss: 0.5627 Sum Loss: 2.0444
Epoch 1/1... Discriminator Loss: 1.3987... Generator Loss: 0.5821 Sum Loss: 1.9808
Epoch 1/1... Discriminator Loss: 1.3951... Generator Loss: 0.6074 Sum Loss: 2.0024
Epoch 1/1... Discriminator Loss: 1.3337... Generator Loss: 0.6803 Sum Loss: 2.0140
Epoch 1/1... Discriminator Loss: 1.3949... Generator Loss: 0.7831 Sum Loss: 2.1780
Epoch 1/1... Discriminator Loss: 1.4658... Generator Loss: 0.6324 Sum Loss: 2.0982
Epoch 1/1... Discriminator Loss: 1.3298... Generator Loss: 0.6141 Sum Loss: 1.9440
Epoch 1/1... Discriminator Loss: 1.3755... Generator Loss: 0.7458 Sum Loss: 2.1213
Epoch 1/1... Discriminator Loss: 1.4568... Generator Loss: 0.6100 Sum Loss: 2.0667
Epoch 1/1... Discriminator Loss: 1.4643... Generator Loss: 0.6771 Sum Loss: 2.1414
Epoch 1/1... Discriminator Loss: 1.3771... Generator Loss: 0.8821 Sum Loss: 2.2592
Epoch 1/1... Discriminator Loss: 1.4961... Generator Loss: 0.7681 Sum Loss: 2.2642
Epoch 1/1... Discriminator Loss: 1.4085... Generator Loss: 0.6474 Sum Loss: 2.0559
Epoch 1/1... Discriminator Loss: 1.4729... Generator Loss: 0.9717 Sum Loss: 2.4446
Epoch 1/1... Discriminator Loss: 1.5114... Generator Loss: 0.7096 Sum Loss: 2.2210
Epoch 1/1... Discriminator Loss: 1.3707... Generator Loss: 0.5598 Sum Loss: 1.9306
Epoch 1/1... Discriminator Loss: 1.4136... Generator Loss: 0.6670 Sum Loss: 2.0806
Epoch 1/1... Discriminator Loss: 1.5361... Generator Loss: 0.5853 Sum Loss: 2.1214
Epoch 1/1... Discriminator Loss: 1.4382... Generator Loss: 0.6875 Sum Loss: 2.1257
Epoch 1/1... Discriminator Loss: 1.3392... Generator Loss: 0.7331 Sum Loss: 2.0723
Epoch 1/1... Discriminator Loss: 1.5369... Generator Loss: 0.4812 Sum Loss: 2.0182
Epoch 1/1... Discriminator Loss: 1.2955... Generator Loss: 0.7880 Sum Loss: 2.0835
Epoch 1/1... Discriminator Loss: 1.4431... Generator Loss: 0.6449 Sum Loss: 2.0880
Epoch 1/1... Discriminator Loss: 1.4048... Generator Loss: 0.8253 Sum Loss: 2.2301
Epoch 1/1... Discriminator Loss: 1.4312... Generator Loss: 0.5718 Sum Loss: 2.0030
Epoch 1/1... Discriminator Loss: 1.4575... Generator Loss: 0.6260 Sum Loss: 2.0835
Epoch 1/1... Discriminator Loss: 1.4100... Generator Loss: 0.6232 Sum Loss: 2.0332
Epoch 1/1... Discriminator Loss: 1.3410... Generator Loss: 0.9096 Sum Loss: 2.2506
Epoch 1/1... Discriminator Loss: 1.4483... Generator Loss: 0.5557 Sum Loss: 2.0040
Epoch 1/1... Discriminator Loss: 1.3349... Generator Loss: 0.6846 Sum Loss: 2.0195
Epoch 1/1... Discriminator Loss: 1.2024... Generator Loss: 1.0573 Sum Loss: 2.2596
Epoch 1/1... Discriminator Loss: 1.6264... Generator Loss: 0.7929 Sum Loss: 2.4193
Epoch 1/1... Discriminator Loss: 1.4016... Generator Loss: 0.6396 Sum Loss: 2.0412
Epoch 1/1... Discriminator Loss: 1.3570... Generator Loss: 0.6244 Sum Loss: 1.9814
Epoch 1/1... Discriminator Loss: 1.6214... Generator Loss: 0.4505 Sum Loss: 2.0719
Epoch 1/1... Discriminator Loss: 1.3619... Generator Loss: 0.8809 Sum Loss: 2.2429
Epoch 1/1... Discriminator Loss: 1.4525... Generator Loss: 0.6594 Sum Loss: 2.1119
Epoch 1/1... Discriminator Loss: 1.4121... Generator Loss: 0.6985 Sum Loss: 2.1105
Epoch 1/1... Discriminator Loss: 1.3496... Generator Loss: 0.8056 Sum Loss: 2.1553
Epoch 1/1... Discriminator Loss: 1.5660... Generator Loss: 0.7352 Sum Loss: 2.3012
Epoch 1/1... Discriminator Loss: 1.5025... Generator Loss: 0.5267 Sum Loss: 2.0292
Epoch 1/1... Discriminator Loss: 1.4850... Generator Loss: 0.6275 Sum Loss: 2.1125
Epoch 1/1... Discriminator Loss: 1.4025... Generator Loss: 0.9023 Sum Loss: 2.3048
Epoch 1/1... Discriminator Loss: 1.3706... Generator Loss: 0.8383 Sum Loss: 2.2089
Epoch 1/1... Discriminator Loss: 1.3764... Generator Loss: 0.5757 Sum Loss: 1.9521
Epoch 1/1... Discriminator Loss: 1.4319... Generator Loss: 0.7723 Sum Loss: 2.2043
Epoch 1/1... Discriminator Loss: 1.3032... Generator Loss: 0.8729 Sum Loss: 2.1761
Epoch 1/1... Discriminator Loss: 1.3987... Generator Loss: 0.6610 Sum Loss: 2.0597
Epoch 1/1... Discriminator Loss: 1.4816... Generator Loss: 0.8962 Sum Loss: 2.3778
Epoch 1/1... Discriminator Loss: 1.3784... Generator Loss: 0.7277 Sum Loss: 2.1061
Epoch 1/1... Discriminator Loss: 1.3665... Generator Loss: 0.5877 Sum Loss: 1.9542
Epoch 1/1... Discriminator Loss: 1.3161... Generator Loss: 0.8253 Sum Loss: 2.1414
Epoch 1/1... Discriminator Loss: 1.3585... Generator Loss: 0.7442 Sum Loss: 2.1027

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.